Zomato Data Analysis

Project Overview

Welcome to the "Zomato Data Analysis" project! This project aims to explore and analyze a dataset containing information about restaurant chains worldwide. The dataset, provided by Zomato and obtained from Kaggle, includes details such as average cost for two, location, votes, aggregate rating, cuisines, country, and rating text.

Dataset

The dataset serves as a valuable resource for understanding the global landscape of restaurants. It facilitates a wide range of analyses, including but not limited to identifying top cuisines, discovering the most expensive restaurants, calculating the average rating for each country, and more.

Data Analysis Process

1. Loading the Raw Data:

   • The initial step involves loading the raw data to kickstart the analysis process.

2. Data Cleaning in Python:

   • Data cleaning is crucial for ensuring the accuracy and reliability of the analysis.
   • Unnecessary columns are dropped to streamline the dataset.
   • Duplicate rows are removed to avoid data redundancy.
   • Individual rows are cleaned, ensuring data integrity.

3. Interactive Dashboard Development:

   • With the data cleaned and prepared, the next step involves developing an interactive dashboard to visualize the insights gained from the analysis.

Key Questions and Analysis

The analysis focuses on answering several key questions:

1. Total Restaurants and Total Cuisines Worldwide:

   • Exploring the overall count of restaurants and cuisines globally.

2. Countries with the Greatest Number of Restaurants on Zomato:

   • Identifying countries with the highest enrollment of restaurants on Zomato.

3. Cities in India with the Greatest Number of "Value for Money" Restaurants:

   • Analyzing which cities in India offer the best value for money dining options.

4. Top 10 Cuisines with the Highest Number of Votes in India:

   • Highlighting the cuisines that receive the most votes in India.

5. Countries with Restaurants Providing Online Delivery:

   • Identifying countries where restaurants offer online delivery services.

6. Locality with the Most Restaurants in Terms of Quantity:

   • Determining the locality with the highest number of restaurants.

7. Restaurants with Good Cuisine and an Average Rating:

   • Showcasing restaurants that excel in both cuisine quality and average rating.

Conclusion

This "Zomato Data Analysis" project provides valuable insights into the global restaurant landscape. The interactive dashboard presents visualizations to make the exploration of the dataset more intuitive. Feel free to explore and contribute to this project, and let the data-driven journey begin!